
"""simple docstring"""



import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A   )     ->	List[str]:





    """simple docstring"""

    _UpperCAmelCase        = TapasConfig.from_json_file(_A   )
    # set absolute/relative position embeddings parameter
    _UpperCAmelCase        = reset_position_index_per_cell

    # set remaining parameters of TapasConfig as well as the model based on the task
    if task == "SQA":
        _UpperCAmelCase        = TapasForQuestionAnswering(config=_A   )
    elif task == "WTQ":
        # run_task_main.py hparams
        _UpperCAmelCase        = 4
        _UpperCAmelCase        = True
        # hparam_utils.py hparams
        _UpperCAmelCase        = 0.664_694
        _UpperCAmelCase        = 0.207_951
        _UpperCAmelCase        = 0.121_194
        _UpperCAmelCase        = True
        _UpperCAmelCase        = True
        _UpperCAmelCase        = False
        _UpperCAmelCase        = 0.0_352_513

        _UpperCAmelCase        = TapasForQuestionAnswering(config=_A   )
    elif task == "WIKISQL_SUPERVISED":
        # run_task_main.py hparams
        _UpperCAmelCase        = 4
        _UpperCAmelCase        = False
        # hparam_utils.py hparams
        _UpperCAmelCase        = 36.4_519
        _UpperCAmelCase        = 0.903_421
        _UpperCAmelCase        = 222.088
        _UpperCAmelCase        = True
        _UpperCAmelCase        = True
        _UpperCAmelCase        = True
        _UpperCAmelCase        = 0.763_141

        _UpperCAmelCase        = TapasForQuestionAnswering(config=_A   )
    elif task == "TABFACT":
        _UpperCAmelCase        = TapasForSequenceClassification(config=_A   )
    elif task == "MLM":
        _UpperCAmelCase        = TapasForMaskedLM(config=_A   )
    elif task == "INTERMEDIATE_PRETRAINING":
        _UpperCAmelCase        = TapasModel(config=_A   )
    else:
        raise ValueError(F"""Task {task} not supported."""   )

    print(F"""Building PyTorch model from configuration: {config}"""   )
    # Load weights from tf checkpoint
    load_tf_weights_in_tapas(_A       ,   _A       ,   _A   )

    # Save pytorch-model (weights and configuration)
    print(F"""Save PyTorch model to {pytorch_dump_path}"""   )
    model.save_pretrained(_A   )

    # Save tokenizer files
    print(F"""Save tokenizer files to {pytorch_dump_path}"""   )
    _UpperCAmelCase        = TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + """vocab.txt"""       ,   model_max_length=5_1_2   )
    tokenizer.save_pretrained(_A   )

    print("""Used relative position embeddings:"""       ,   model.config.reset_position_index_per_cell   )


if __name__ == "__main__":
 a       :						List[Any]       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
 )
 parser.add_argument(
     '''--reset_position_index_per_cell''',
     default=False,
     action='''store_true''',
     help='''Whether to use relative position embeddings or not. Defaults to True.''',
 )
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--tapas_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained TAPAS model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a       :						Union[str, Any]       		=		parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )

"""simple docstring"""



import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = OmegaConf.load(_A   )
    if display:
        print(yaml.dump(OmegaConf.to_container(_A   )   )   )
    return config


def 			_UpperCamelCase						(    _A       ,   _A=None       ,   _A=None   )     ->	List[Any]:





    """simple docstring"""

    if conf_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.yaml"""
    _UpperCAmelCase        = load_config(_A       ,   display=_A   )
    _UpperCAmelCase        = VQModel(**config.model.params   )
    if ckpt_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.pt"""
    _UpperCAmelCase        = torch.load(_A       ,   map_location=_A   )
    if ".ckpt" in ckpt_path:
        _UpperCAmelCase        = sd["""state_dict"""]
    model.load_state_dict(_A       ,   strict=_A   )
    model.to(_A   )
    del sd
    return model


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = model.encode(_A   )
    print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""   )
    _UpperCAmelCase        = model.decode(_A   )
    return xrec


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = string.rsplit("""."""       ,   1   )
    if reload:
        _UpperCAmelCase        = importlib.import_module(_A   )
        importlib.reload(_A   )
    return getattr(importlib.import_module(_A       ,   package=_A   )       ,   cls   )


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    if "target" not in config:
        raise KeyError("""Expected key `target` to instantiate."""   )
    return get_obj_from_str(config["""target"""]   )(**config.get("""params"""       ,   {}   )   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=True       ,   _A=True   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = instantiate_from_config(_A   )
    if sd is not None:
        model.load_state_dict(_A   )
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}







def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A   )     ->	Optional[Any]:





    """simple docstring"""

    if ckpt:
        _UpperCAmelCase        = torch.load(_A       ,   map_location="""cpu"""   )
        _UpperCAmelCase        = pl_sd["""global_step"""]
        print(F"""loaded model from global step {global_step}."""   )
    else:
        _UpperCAmelCase        = {"""state_dict""": None}
        _UpperCAmelCase        = None
    _UpperCAmelCase        = load_model_from_config(config.model       ,   pl_sd["""state_dict"""]       ,   gpu=_A       ,   eval_mode=_A   )["""model"""]
    return model, global_step